Recent advancements and applications of deep learning in heart failure: Α systematic review

• Published in: Computers in biology and medicine Vol. 176; p. 108557
• Main Authors: Petmezas, Georgios, Papageorgiou, Vasileios E., Vassilikos, Vasileios, Pagourelias, Efstathios, Tsaklidis, George, Katsaggelos, Aggelos K., Maglaveras, Nicos
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2024; Elsevier Limited
• Subjects: Algorithms; Artificial intelligence; Cardiac arrhythmia; Cardiomyopathy; Cardiovascular disease; Cardiovascular diseases; Classification; Clinical outcomes; CNN; Comorbidity; Congestive heart failure; Deep Learning; Dilated cardiomyopathy; Electronic health records; Evaluation; Global health; Health services; Heart diseases; Heart failure; Heart Failure - diagnosis; Humans; Hypertrophic cardiomyopathy; Image analysis; Image classification; Image processing; Ischemia; Ischemic cardiomyopathy; Machine learning; Medical equipment; Medical personnel; Mortality; Neural networks; Patients; Precision medicine; Public health; Quality of life; Research methodology; ResNet; Risk analysis; Risk assessment; RNN; Statistical methods; Systematic review; Transformers; Heart failure; Deep learning; CNN; RNN; Dilated cardiomyopathy; Ischemic cardiomyopathy; Hypertrophic cardiomyopathy; Transformers; Artificial intelligence; ResNet
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108557

Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care. •A comprehensive review of innovative deep learning methods applied on heart failure data.•Compiles diverse, open-access heart failure data sources to assist future research.•Presents the most frequently used data preprocessing and slitting strategies.•Analyzes cardiomyopathies' impact in heart failure, offering new insights.•Outlines key open research problems and future directions in the application of deep learning for heart failure assessment.